3. A Suite of Products that allow you to Predict Outcomes, Prescribe Actions and Automate Decisions

4. Azure Data FactoryAzure Data CatalogAzure Event HubsAzure Data LakeAzure SQL DB, Data Warehouse, DocumentDBAzure Machine LearningAzure HDInsightAzure Stream Analytics Power BI Microsoft Azure Cortana

5. Determining the actual problems to solve Identifying and vetting data sources Defining the data path Create Cleansing and Homogenizing Processes Create Feature Selection process (where needed) Create Computation Processes (Predictive or Classification) Create Output and Presentation Instruments

7. Ingest Transform PPT REM 12 Store Analyze Act

8. Azure Platform IaaS, PaaS, SaaS Azure Storage Tools and Programming Data Science and Architecture Overview Designing Solutions CAP Implementing solutions Core Ingest Prepare Features Model and Process Deploy Data Ingress Azure Storage and On-Prem Data Catalog Data Foundation Data Prep with R SQL-R/Srvr/ML/Hadoop Data Prep with HDI Hadoop Eco Data Lake Storage and Processing Structured Data RDBMS/Warehouse Semi -Structured Data NoSQL Data Factory Orchestration Data Prep with Azure ML Data Grooming Stream Analytics Query for Result Event Hubs IoT Feature Eng. with R SQL-R/Srvr/ML/Hadoop Feature Eng. with HDI Mahout Feature Eng. with Azure ML Deep Selection Model/Process with R SQL-R/Srvr/ML/Hadoop Model/Process with HDI Processing at Scale Model/Process with Azure ML Algorithm Selection, Evaluation API’s Publishing, Using Power BI Visualizations Programming Cortana Natural Language Processing The Cortana Analytics Process: Designing a Solution Data Prep with Python ML/Hadoop/VS Feature Eng. with Python ML/Hadoop/VS Model/Process with Python ML/Hadoop/VS

9. CIS Example – Customer Churn Model Call Log Files Customer Table Call Log Files Customer Table Customer Churn Table Azure Data Factory: Data Sources Customers Likely to Churn Customer Call Details Transform & AnalyzePublishIngest

10. https://gallery.cortanaanalytics.com/collections